{"id":"W2041040308","doi":"10.1021/jm051209w","title":"Similarity Based Virtual Screening:  A Tool for Targeted Library Design","year":2006,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Union of Biochemistry and Molecular Biology","keywords":"Virtual screening; Drug discovery; Chemistry; Computational biology; High-throughput screening; Similarity (geometry); Chemical library; Drug; Throughput; Small molecule; Combinatorial chemistry; Pharmacology; Computer science; Biochemistry; Biology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001297321,0.001278232,0.001875979,0.002406571,0.0004960629,0.001245927,0.001619189,0.0008688486,0.005238625],"category_scores_gemma":[0.002402335,0.0005900462,0.001211845,0.00216053,0.0005290426,0.000794596,0.001472869,0.0009925058,0.002024468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004758253,"about_ca_system_score_gemma":0.0007707767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006340871,"about_ca_topic_score_gemma":0.0006381016,"domain_scores_codex":[0.9988347,0.0004584396,0.0000515749,0.0001089824,0.0004830824,0.00006327133],"domain_scores_gemma":[0.9993678,0.0003713846,0.00005893407,0.00009668271,0.00006253632,0.00004263679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008566984,0.000801528,0.001927958,0.001185037,0.0008172405,0.0009545179,0.0001462218,0.191984,0.04784209,0.04057785,0.03595383,0.6769531],"study_design_scores_gemma":[0.0005494685,0.0007894728,0.001248033,0.00007790112,0.0002331932,0.001002007,0.00004736347,0.8371372,0.05071703,0.05407093,0.05400499,0.0001225087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01615814,0.0008720735,0.9532161,0.000279994,0.00008111021,0.0006261928,0.002183812,0.01996403,0.006618502],"genre_scores_gemma":[0.1771957,0.001459233,0.8117002,0.0002908146,0.00005445662,0.001660739,0.003607666,0.0006285407,0.00340259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005238625,"threshold_uncertainty_score":0.01752496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271654310004807,"score_gpt":0.273841337082438,"score_spread":0.2466759060819573,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}